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Generative AI

Generate the draft. Keep the evidence attached.

Werkon builds generative AI around an output contract: what may be created, which sources and rights apply, how claims are checked, who can approve use, what must be rejected, and how quality and harm are monitored after release.

Output contract

Define what the output means before generating it.

The generative boundary follows the output from authorized input through context assembly, generation, deterministic checks, source and domain verification, approval, release, correction, and retention. It also defines cases that should never enter the model. Compare knowledge management for source-grounded answers with AI agent development when the system must also act through tools.

Inputs

Purpose and consequence
Users, task, output form, factual and creative elements, audience, downstream use, affected parties, error consequences, current method, desired improvement, human role, and prohibited use.
Sources and rights
Authoritative records, retrieval collections, ownership, license, consent, privacy, sensitivity, freshness, provenance, retention, deletion, attribution, exclusion, and content that may not be used for training or generation.
Generation and constraints
Model and provider, version, prompts, tools, retrieval, structured schemas, length, style, terminology, policy, forbidden content, factuality rules, citations, uncertainty, cost, latency, and fallback.
Evaluation and operation
Representative and adversarial cases, source-support checks, reviewers, approval, rejection, review time, corrections, user feedback, incidents, monitoring, model and source changes, audit, and retirement.

Outputs

Use and output policy
Allowed users, tasks, sources, data classes, output forms, claims, tools, publication paths, human decisions, prohibited content, disclosures, attribution, retention, and escalation.
Grounded generation path
A working flow that selects authorized context, preserves provenance, constrains generation, validates structure and policy, exposes supporting evidence, routes review, and records release or rejection.
Evaluation and failure report
Results for ordinary, difficult, conflicting, missing-source, stale, multilingual, adversarial, harmful, private, unsupported, overlong, malformed, and out-of-scope cases plus reviewer burden and residual limits.
Operating and correction pack
Version and source inventory, monitoring, rejection and review queues, feedback, incident and correction process, prompt and model change gates, provider fallback, audit, retention, published-output trace, rollback, and handover.

Generation path

Build the rejection path before widening output.

The first useful slice handles one output type and one source boundary. It earns wider scope only after unsupported claims, unsafe content, missing evidence, permission errors, and reviewer overload are visible and recoverable.

  1. 01

    Constrain the task

    Separate factual claims, transformations, recommendations, code, and creative material. Define the audience, sources, output schema, prohibited uses, consequence, reviewers, measures, rejection reasons, and release authority.

  2. 02

    Assemble authorized evidence

    Select the minimum approved sources and tools, preserve provenance and freshness, enforce user and tenant scope, exclude prohibited data, and make missing or conflicting evidence explicit.

  3. 03

    Generate within constraints

    Use versioned prompts, retrieval, schemas, policies, tool boundaries, and output limits. Keep source references aligned with claims and reject malformed, unsupported, private, or prohibited results before review.

  4. 04

    Challenge output and review

    Test representative, difficult, adversarial, stale, conflicting, multilingual, and out-of-scope cases, measure source support and review burden, and give reviewers evidence, edit, reject, and escalation controls.

  5. 05

    Release, trace, and correct

    Stage use, label or disclose output where appropriate, preserve version and evidence, monitor failures and human work, accept feedback, correct released content, re-evaluate changes, and keep a non-generative fallback.

Output pattern

Match verification to what the model is being asked to create.

A summary, source-backed answer, working draft, and creative concept do not carry the same truth claim. Each needs a different evidence and approval path.

01Meaning should remain anchored

Transform a known source

Summarize, translate, restructure, classify, or extract from an identified source when the output can be checked against the complete input and omissions or changes are visible.

Evidence: Source identity and version, authorized fields, coverage checks, terminology, omissions, transformation rules, reviewer samples, and link back to the input.

02The response depends on current records

Answer from retrieved evidence

Retrieve scoped sources and compose an answer when users need synthesis across records, with claim-level support, freshness, access control, and a clear no-answer path.

Evidence: Query set, retrieval recall, permission tests, source ranking, claim support, conflicting-source behavior, citation usability, no-answer accuracy, and latency.

03A person remains the author or decision maker

Prepare a reviewed draft

Draft messages, documents, code, plans, or responses when an accountable person can inspect the evidence and full result before it is sent, merged, published, or used for action.

Evidence: Reviewer role, diff or preview, source panel, edit and reject actions, workload, error samples, approval record, and blocked automated release.

04Novel content is the stated purpose

Create deliberate synthetic media

Generate concepts or media when factual grounding is not the main claim, while preserving consent, rights, prohibited-subject rules, provenance, disclosure, review, and downstream-use limits.

Evidence: Input rights, subject consent where needed, style and identity boundaries, safety tests, provenance record, disclosure policy, reviewer, storage, and takedown path.

Generation controls

Fluent output still has to prove where it came from.

Generative systems can present false, unsupported, private, harmful, or unauthorized material in a convincing form. Controls should attach evidence and authority to the output instead of relying on tone or reviewer intuition.

Sources remain inspectable
Preserve source identity, version, location, access context, freshness, and the exact passages or records supporting factual output. Reject invented, mismatched, inaccessible, or stale references.
Deterministic rules surround generation
Use ordinary code for permissions, required fields, numeric calculations, identifiers, policy limits, schema validation, prohibited actions, retention, and release state rather than asking the model to enforce them.
Review carries real authority
Give the authorized reviewer enough context, evidence, time, domain support, edit and reject controls, and escalation. Measure corrections, missed errors, duplicated checking, fatigue, and queue delay.
Released output can be traced
Record model, prompt, sources, tools, policy, reviewer, time, and destination as appropriate. Support feedback, correction, withdrawal, incident review, provider change, and disclosure or provenance requirements.

Engagement fit

Use generative AI when controlled variation adds value to an owned output path.

Good reason to begin

  • A repeated summarization, transformation, answering, drafting, coding, or creative task has a specific audience, source boundary, output contract, and useful review or release path.
  • The organization can supply lawful sources, representative cases, domain review, user feedback, and owners for model, prompt, policy, data, and publication changes.
  • A narrow first slice can attach evidence, reject unsupported output, measure reviewer load, and retain a manual or deterministic fallback.
  • Generated output can remain advisory or reviewed wherever factual, legal, financial, safety, employment, health, or other consequential use requires accountable human authority.

Resolve before beginning

  • The requested value is an exact rule, calculation, lookup, record update, or fixed document transformation that ordinary software can perform more reliably.
  • Sources, rights, consent, privacy, audience, publication authority, prohibited uses, or correction responsibility are unresolved.
  • The plan assumes citations guarantee truth, reviewers will catch every error, synthetic-content detectors are conclusive, or a provider safety layer owns the application decision.
  • There is no representative evaluation set, rejection path, feedback mechanism, audit, fallback, or team prepared to operate model and source changes.

Source basis

Sources behind the control model.

[ WORKFLOW / SYSTEMS AUDIT ]
THE FIRST ENGAGEMENT

Start with one real workflow

A Systems Audit is the usual starting point. If the opportunity is already clear, we can move directly into a focused build.

Show Us the WorkflowStart with the free automation readiness checklist

OBSERVEQUANTIFYDECIDEBUILD

Continue with the work

Choose the next useful path.

Stay with the operating question. The technology can wait until the work is clear.

01

Explore AI services

Compare generative AI with custom systems, models, agents, automation, integration, language processing, vision, deployment, and support.

Open path02

Build a custom AI system

Own the complete application path when generation must connect to sources, rules, permissions, interfaces, actions, evaluation, and operation.

Open path03

Develop or adapt an AI model

Compare model adaptation with existing services, retrieval, prompting, and simpler baselines under protected evaluation.

Open path04

Connect AI to existing systems

Define the identity, data, action, failure, and reconciliation contracts needed around a generative capability.

Open path